From a63ee8ba14aad0e364e928399b24196c72a4217f Mon Sep 17 00:00:00 2001
From: jvech <jmvalenciae@unal.edu.co>
Date: Wed, 4 Sep 2024 22:15:23 -0500
Subject: fix: ml_network_train() refactored

---
 src/nn.c | 31 ++++++++++++++++++++++---------
 1 file changed, 22 insertions(+), 9 deletions(-)

(limited to 'src/nn.c')

diff --git a/src/nn.c b/src/nn.c
index 867819c..19076bb 100644
--- a/src/nn.c
+++ b/src/nn.c
@@ -29,6 +29,8 @@
 #include "util.h"
 #include "nn.h"
 
+
+struct Cost load_loss(struct Configs cfg);
 static void dataset_shuffle_rows(
         double *inputs, size_t in_shape[2],
         double *labels, size_t lbl_shape[2]);
@@ -73,15 +75,19 @@ void nn_network_predict(
 }
 
 void nn_network_train(
-        Layer network[], size_t network_size,
+        Layer network[], struct Configs ml_configs,
         double *input, size_t input_shape[2],
-        double *labels, size_t labels_shape[2],
-        struct Cost cost, size_t epochs,
-        size_t batch_size, double alpha,
-        bool shuffle)
+        double *labels, size_t labels_shape[2])
 {
     assert(input_shape[0] == labels_shape[0] && "label samples don't correspond with input samples\n");
 
+    size_t epochs = ml_configs.epochs;
+    size_t batch_size = ml_configs.batch_size;
+    size_t network_size = ml_configs.network_size;
+    double alpha = ml_configs.alpha;
+    bool shuffle = ml_configs.shuffle;
+    struct Cost cost = load_loss(ml_configs);
+
     double **outs = calloc(network_size, sizeof(double *));
     double **zouts = calloc(network_size, sizeof(double *));
     double **weights = calloc(network_size, sizeof(double *));
@@ -204,19 +210,19 @@ void nn_backward(
                 double *out_prev = Outs[l - 1] + sample * network[l-1].neurons;
                 double *dcost_out = dcost_outs + sample * network[l].neurons;
                 nn_layer_out_delta(delta, dcost_out, zout, network[l].neurons, network[l].activation.dfunc);
-                nn_layer_backward(weights[l], bias[l], weights_shape, delta, out_prev, network[l], alpha);
+                nn_layer_backward(weights[l], bias[l], weights_shape, delta, out_prev, alpha);
             } else if (l == 0) {
                 size_t weights_next_shape[2] = {network[l+1].input_nodes, network[l+1].neurons};
                 double *zout = Zout[l] + sample * network[l].neurons;
                 double *input = Input + sample * input_shape[1];
                 nn_layer_hidden_delta(delta, delta_next, zout, weights[l+1], weights_next_shape, network[l].activation.dfunc);
-                nn_layer_backward(weights[l], bias[l], weights_shape, delta, input, network[l], alpha);
+                nn_layer_backward(weights[l], bias[l], weights_shape, delta, input, alpha);
             } else {
                 size_t weights_next_shape[2] = {network[l+1].input_nodes, network[l+1].neurons};
                 double *zout = Zout[l] + sample * network[l].neurons;
                 double *out_prev = Outs[l - 1] + sample * network[l-1].neurons;
                 nn_layer_hidden_delta(delta, delta_next, zout, weights[l+1], weights_next_shape, network[l].activation.dfunc);
-                nn_layer_backward(weights[l], bias[l], weights_shape, delta, out_prev, network[l], alpha);
+                nn_layer_backward(weights[l], bias[l], weights_shape, delta, out_prev, alpha);
             }
             memmove(delta_next, delta, weights_shape[1] * sizeof(double));
         }
@@ -241,7 +247,7 @@ nn_backward_error:
 void nn_layer_backward(
         double *weights, double *bias, size_t weights_shape[2],
         double *delta, double *out_prev,
-        Layer layer, double alpha)
+        double alpha)
 {
     // W_next = W - alpha * out_prev @ delta.T
     cblas_dger(CblasRowMajor, weights_shape[0], weights_shape[1], -alpha,
@@ -544,6 +550,13 @@ double get_avg_loss(
     return sum / shape[0];
 }
 
+struct Cost load_loss(struct Configs cfg)
+{
+    if (!strcmp("square", cfg.loss)) return NN_SQUARE;
+    die("load_loss() Error: Unknown '%s' loss function", cfg.loss);
+    exit(1);
+}
+
 #ifdef NN_TEST
 /*
  * compile: clang -Wall -Wextra -g -DNN_TEST -o objs/test_nn src/util.c src/nn.c $(pkg-config --libs-only-l blas) -lm
-- 
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